Evidence map›Paper›PMID 33061715›Full record

ArticleRisk management and healthcare policy2020

The Knowledge and Attitude of the Community from the Aseer Region, Saudi Arabia, Toward COVID-19 and Their Precautionary Measures Against the Disease.

Arwa Khaled, Ayesha Siddiqua, Soha Makki

Abstract read
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Article in Risk management and healthcare policy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
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23citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Arwa KhaledDepartment of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha, Saudi Arabia.ORCID 0000-0002-0064-7870
Ayesha SiddiquaDepartment of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha, Saudi Arabia.ORCID 0000-0001-9882-7695
Soha MakkiDepartment of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha, Saudi Arabia.ORCID 0000-0002-0097-6404

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCommunity adherence to current control measures is one of the vital factors that determines the success of controlling the spread of COVID-19. This adherence depends on the knowledge and attitude of a community toward COVID-19 and their precautionary measures against the disease.

objectiveThis study aims to evaluate the knowledge and attitude of the community from the Aseer Region, Saudi Arabia, toward COVID-19 and their precautionary measures against the disease.

methodsThe descriptive cross-sectional study involved an online sample of participants from the Aseer Region, Saudi Arabia, who were recruited based on a nonprobability convenience sampling method from April to May 2020. The self-administered online questionnaire comprised four parts: the first included the demographic details of the participants; the second consisted of 11 closed-ended questions that assessed the participants' knowledge of COVID-19; the third contained six closed-ended questions that assessed the participants' overall attitude toward COVID-19; and the fourth comprised six closed-ended questions on the participants' precautionary measures against COVID-19. The data were analyzed using Statistisoftware SPSS 21.0.

resultsAltogether, 740 participants completed the online questionnaire. The mean knowledge score concerning COVID-19 was 8.08 ± 1.765. Graduate participants scored significantly higher for all knowledge items than those from other educational levels. Furthermore, the main source of COVID-19 information was the Ministry of Health website 661 (89.3%). The mean attitude score regarding COVID-19 was 9.74 ± 1.935, whereas the mean precautionary measures score was 10.09 ± 1.91. University-educated participants scored significantly higher for all knowledge items than those from other educational levels. Female participants had significantly higher precautionary measures scores than male participants.

conclusionThis study confirms that residents of the Aseer Region have high knowledge of, demonstrate a positive attitude toward, and use appropriate precautionary measures against COVID-19, which is associated with obtaining information about the virus from the Ministry of Health website.

Indexed as

attitudecoronavirusknowledgeprecautionary measures

Identifiers

PMID33061715
PMCPMC7533267

What OpenQuestion holds

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LicenceCC BY-NC
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.